Monte Carlo

APRIL 4, 2023

Data mesh vs data warehouse is an interesting framing because it is not necessarily a binary choice depending on what exactly you mean by data warehouse (more on that later). Despite their differences, however, both approaches require high-quality, reliable data in order to function. What is a Data Mesh?

Data Warehouse 52

Data Warehouse Data Governance Data Architecture 52

Data Engineering Podcast

FEBRUARY 8, 2021

Summary With all of the tools and services available for building a data platform it can be difficult to separate the signal from the noise. In this episode Zeeshan Qureshi and Michelle Ark share their experiences using DBT to manage the data warehouse for Shopify.

Data Warehouse 100

Data Warehouse Building BI SQL 100

Data Engineering Podcast

APRIL 19, 2021

Summary Most of the time when you think about a data pipeline or ETL job what comes to mind is a purely mechanistic progression of functions that move data from point A to point B. Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Data Pipeline 100

Data Pipeline Machine Learning Data Warehouse Datasets 100

Data Engineering Podcast

APRIL 23, 2023

Meroxa was created to enable teams of all sizes to deliver real-time data applications. In this episode DeVaris Brown discusses the types of applications that are possible when teams don't have to manage the complex infrastructure necessary to support continuous data flows.

Data Lake 277

Data Lake Kafka Machine Learning Data Warehouse 277

Data Engineering Podcast

JULY 17, 2022

Summary There are extensive and valuable data sets that are available outside the bounds of your organization. Whether that data is public, paid, or scraped it requires investment and upkeep to acquire and integrate it with your systems. Atlan is the metadata hub for your data ecosystem.

Data Management 130

Data Management Management Metadata MongoDB 130

Data Engineering Podcast

JUNE 1, 2021

Summary Every part of the business relies on data, yet only a small team has the context and expertise to build and maintain workflows and data pipelines to transform, clean, and integrate it. RudderStack’s smart customer data pipeline is warehouse-first.

Data Pipeline 100

Data Pipeline Data Warehouse Data Workflow Data 100

Data Engineering Podcast

JANUARY 18, 2021

Summary The data warehouse has become the central component of the modern data stack. This is an interesting conversation about the importance of the data warehouse and how it can be used beyond just internal analytics. And don’t forget to thank them for their continued support of this show!

Data Warehouse 100

Data Warehouse BI Data Data Pipeline 100

Data Engineering Podcast

FEBRUARY 4, 2024

In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex.

SQL 173

SQL Data Lake High Quality Data Data Pipeline 173

Data Engineering Podcast

JANUARY 21, 2024

A substantial amount of the data that is being managed in these systems is related to customers and their interactions with an organization. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex.

Data Lake 147

Data Lake High Quality Data NoSQL Data Warehouse 147

Data Engineering Podcast

OCTOBER 14, 2019

Summary Managing a data warehouse can be challenging, especially when trying to maintain a common set of patterns. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform.

Data Warehouse 100

Data Warehouse PostgreSQL AWS Programming Language 100

Ascend.io

MAY 31, 2023

In the modern world of data engineering, two concepts often find themselves in a semantic tug-of-war: data pipeline and ETL. Fast forward to the present day, and we now have data pipelines. Data Ingestion Data ingestion is the first step of both ETL and data pipelines.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

Data Engineering Podcast

JUNE 9, 2024

Summary Streaming data processing enables new categories of data products and analytics. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is supported by Code Comments, an original podcast from Red Hat. Data lakes are notoriously complex.

Process 147

Process Data Lake High Quality Data Machine Learning 147

Monte Carlo

AUGUST 25, 2023

Modern companies are ingesting, storing, transforming, and leveraging more data to drive more decision-making than ever before. Data teams need to balance the need for robust, powerful data platforms with increasing scrutiny on costs. But, the options for data storage are evolving quickly. Let’s dive in.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Monte Carlo

FEBRUARY 6, 2023

So, you’re planning a cloud data warehouse migration. But be warned, a warehouse migration isn’t for the faint of heart. As you probably already know if you’re reading this, a data warehouse migration is the process of moving data from one warehouse to another. A worthy quest to be sure.

Data Warehouse 52

Data Warehouse AWS Data Validation Data 52

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. What is Data Warehouse? .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

Data Engineering Podcast

JUNE 25, 2023

Summary Data transformation is a key activity for all of the organizational roles that interact with data. Because of its importance and outsized impact on what is possible for downstream data consumers it is critical that everyone is able to collaborate seamlessly. Can you describe what SQLMesh is and the story behind it?

Data Engineering 225

Data Engineering Data Engineer Python Engineering 225

Data Engineering Podcast

APRIL 9, 2023

Summary The customer data platform is a category of services that was developed early in the evolution of the current era of cloud services for data processing. Now that the data warehouse has taken center stage a new approach of composable customer data platforms is emerging.

Data Lake 147

Data Lake Data Warehouse Machine Learning Data 147

Ascend.io

APRIL 28, 2023

Data pipelines are having a moment — at least, that is, within the data world. That’s because as more and more businesses are adopting a data-driven mindset, the movement of data into and within organizations has never been a bigger priority.

Data Pipeline 52

Data Pipeline Data Data Warehouse Data Engineering 52

Data Engineering Weekly

MAY 26, 2024

link] Meta: Composable data management at Meta Meta writes about its transition to a composable data management system to improve interoperability, reusability, and engineering efficiency. seconds, enhancing real-time sports data analytics efficiency! The author highlights some of the key missing features from S3.

Data Engineering 76

Data Engineering Data Engineer Engineering Google Cloud 76

Data Engineering Podcast

JUNE 4, 2023

Summary A significant portion of the time spent by data engineering teams is on managing the workflows and operations of their pipelines. Agile Data Engine is a platform designed to handle the infrastructure side of the DataOps equation, as well as providing the insights that you need to manage the human side of the workflow.

Data Engineering 130

Data Engineering Data Engineer Engineering Data Lake 130

Data Engineering Podcast

MAY 28, 2023

Summary Building a data team is hard in any circumstance, but at a startup it can be even more challenging. The requirements are fluid, you probably don't have a lot of existing data talent to manage the hiring and onboarding, and there is a need to move fast. What are the concepts that the new hire needs to know?

Data Lake 162

Data Lake Machine Learning Data Warehouse Education 162

Data Engineering Podcast

MAY 14, 2023

In this episode the host Tobias Macey shares his reflections on recent experiences where the abstractions leaked and some observances on how to deal with that situation in a data platform architecture. What do you have planned for the future of your data platform? When is ELT the wrong choice?

Data Lake 147

Data Lake Machine Learning Data Warehouse AWS 147

Data Engineering Podcast

DECEMBER 28, 2022

Summary With all of the messaging about treating data as a product it is becoming difficult to know what that even means. Vishal Singh is the head of products at Starburst which means that he has to spend all of his time thinking and talking about the details of product thinking and its application to data.

Data Lake 130

Data Lake Data Warehouse Data Pipeline MongoDB 130

Data Engineering Podcast

NOVEMBER 16, 2020

Summary The core mission of data engineers is to provide the business with a way to ask and answer questions of their data. This often takes the form of business intelligence dashboards, machine learning models, or APIs on top of a cleaned and curated data set.

Data Management 100

Data Management Management BI Business Intelligence 100

DataKitchen

JULY 27, 2023

The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure. While working in Azure with our customers, we have noticed several standard Azure tools people use to develop data pipelines and ETL or ELT processes. We counted ten ‘standard’ ways to transform and set up batch data pipelines in Microsoft Azure.

Data Pipeline 52

Data Pipeline BI Machine Learning Data Preparation 52

Data Engineering Podcast

DECEMBER 25, 2022

Summary Encryption and security are critical elements in data analytics and machine learning applications. We have well developed protocols and practices around data that is at rest and in motion, but security around data in use is still severely lacking. Sign up free at dataengineeringpodcast.com/rudder Build Data Pipelines.

Machine Learning 130

Machine Learning Systems Data Lake Data Warehouse 130

Data Engineering Podcast

DECEMBER 16, 2018

Summary Every business needs a pipeline for their critical data, even if it is just pasting into a spreadsheet. As the organization grows and gains more customers, the requirements for that pipeline will change. Can you start by sharing your definition of a data pipeline? What are your thoughts on that approach?

Data Pipeline 100

Data Pipeline Data Lake Data Warehouse Python 100

Data Engineering Podcast

JANUARY 15, 2023

Summary The modern data stack has made it more economical to use enterprise grade technologies to power analytics at organizations of every scale. Unfortunately it has also introduced new overhead to manage the full experience as a single workflow. Missing data? Start trusting your data with Monte Carlo today!

Coding 130

Coding Building PostgreSQL Data Lake 130

Data Engineering Podcast

MAY 21, 2023

Summary Batch vs. streaming is a long running debate in the world of data integration and transformation. In this episode David Yaffe and Johnny Graettinger share the story behind the business and technology and how you can start using it today to build a real-time data lake without all of the headache.

Data Lake 162

Data Lake Machine Learning Kafka Data Warehouse 162

Data Engineering Podcast

DECEMBER 18, 2022

Summary One of the reasons that data work is so challenging is because no single person or team owns the entire process. This introduces friction in the process of collecting, processing, and using data. In order to reduce the potential for broken pipelines some teams have started to adopt the idea of data contracts.

Metadata 130

Metadata Business Intelligence Data Lake BI 130

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. As data is expanding exponentially, organizations struggle to harness digital information's power for different business use cases.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Data Engineering Podcast

JUNE 17, 2023

In data systems one of the core architectural exercises is data modeling, which can have significant impacts on what is and is not possible for downstream use cases. By incorporating column-level lineage in the data modeling process it encourages a more robust and well-informed design.

Data Lake 130

Data Lake Metadata Machine Learning Data Architecture 130

Data Engineering Podcast

JANUARY 8, 2023

Summary Managing end-to-end data flows becomes complex and unwieldy as the scale of data and its variety of applications in an organization grows. Part of this complexity is due to the transformation and orchestration of data living in disparate systems. Missing data? Stale dashboards?

PostgreSQL 130

PostgreSQL Data Lake Data Warehouse BI 130

Data Engineering Podcast

JUNE 11, 2023

Summary Data engineering is all about building workflows, pipelines, systems, and interfaces to provide stable and reliable data. Your data can be stable and wrong, but then it isn't reliable. Confidence in your data is achieved through constant validation and testing.

Project 130

Project Building Data Lake Machine Learning 130

ProjectPro

FEBRUARY 16, 2023

ETL is a critical component of success for most data engineering teams, and with teams harnessing it with the power of AWS, the stakes are higher than ever. Data Engineers and Data Scientists require efficient methods for managing large databases, which is why centralized data warehouses are in high demand.

AWS 52

AWS Data Management ETL Tools Management 52

Ascend.io

APRIL 12, 2023

Right now, at this moment, are you prepared to act on your company’s data? So when we talk about making data usable, we’re having a conversation about data integrity. Data integrity is the overall readiness to make confident business decisions with trustworthy data, repeatedly and consistently. If not, why?

Data Pipeline 52

Data Pipeline Data Integration Datasets Data 52

Data Engineering Podcast

NOVEMBER 27, 2022

Summary The most expensive part of working with massive data sets is the work of retrieving and processing the files that contain the raw information. FeatureBase (formerly Pilosa) avoids that overhead by converting the data into bitmaps. RudderStack helps you build a customer data platform on your warehouse or data lake.

Data Lake 100

Data Lake Data Warehouse MongoDB Data Pipeline 100